Some 1/2 hour segments have NAs for all values (how did he handle them?) Also some don’t have all NAs - which is interesting… Finally, some of the final rows are all NAs.
tc_rad %>% is.na %>% apply(1, sum) %>% head(100)
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [19] 0 0 0 75 76 77 78 0 0 0 0 0 0 0 0 0 0 0
## [37] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [55] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [73] 120 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [91] 0 0 0 0 0 0 0 0 0 0
# interestingly some don't completely have all NAs...
some_na <- tc_rad %>% is.na %>% apply(1, sum) %>% sapply(function(v) v> 0)
na_grouping <- some_na %>% cumsum()
na_grouping %>% table # let's look at 9
## .
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## 21 1 1 1 48 48 21 27 48 48 21 27 32 1 1 1 1 1 1 1 1 1 1 1 1 1
## 26 27 28 29 30
## 1 1 1 1 1
This visual is a the 9th segment (relative to NAs) - and is not the overall full time span of the hurricane.
Rapid intensity time (August 26-28)